1. Field of the Invention
The present invention relates to waveform signal analyzers that carry out time-frequency analysis by performing wavelet transformation on waveform signals, and more particularly relates to waveform signal analyzers that may detect the periodicity of a waveform signal, divide the waveform signal using those periods and readily compare the wavelet transformation results of the divided waveform signal.
2. Description of the Related Art
The FFT analyzer that performs FFT (Fast Fourier Transformation) on waveform signals is known as an effective prior art of waveform signal analyzer. This is a device that analyzes and displays the frequency components contained in input waveforms as Fourier spectrum strengths, and may check from the Fourier spectrum distribution whether any abnormal frequency components are contained in the input waveforms.
However, since FFT is an analysis method that has no time (phase) information in the transformation results, it is limited to displaying the distribution of the total values of every waveform component contained within an analysis target time for the input waveform, and may not obtain instant of generation information for the various frequency components.
In prior art, when it was wished to know the time series variation of a frequency component, time-frequency analysis was achieved by slicing the waveform that was the target of analysis into extremely small periods of time, using short-time Fourier transformation employing a window function, and displaying the Fourier spectrum at each instant of time arranged in time series along the time axis.
Also, in recent years, wavelet transformation has been proposed and is becoming widespread as a method of time-frequency analysis that may, like short-time Fourier transformation, handle both time information and frequency information.
Wavelet transformation is a signal treatment method that, while independently converting the scale parameter (frequency) and the shift parameter (time) of a location function (that is to say, a localized function) known as the mother wavelet, obtains inner products of the mother wavelet and the target waveform and transforms the target waveform into a time-frequency spectrum.
The advantage of wavelet transformation is that it may find the instants of generation and the strengths of the diverse frequency components present within the target waveform.
As to display methods for the transformation results of wavelet transformation, the time-frequency distribution may be produced as, for example, a three-dimensional display in which the strength axis is raised from a time-frequency plane, or a contour map-like display in which strengths are color-coded step-wise on a time-frequency plane. This type of display is extremely effective for detecting and distinguishing characteristic components of the target waveform.
Here, when the input waveform taken as the target for time-frequency analysis has the characteristic of being synchronized with some pilot signal or other, such as a power source waveform, the correlation between the phase of the pilot signal and the analysis target waveform may be accurately studied by input waveform division and wavelet transformation with the period of that signal. Consequently, it is effective to perform time-frequency analysis by selecting the period of the pilot signal as one interval (one unit) of the analysis time range.
Also, when it is desired to detect abnormalities within the analysis target waveform, an effective method is to carry out respective wavelet transformation of an analysis target waveform that has been continuously divided as stated above and to carry out visual comparison by making those wavelet transformation results visible. As practical methods for rendering visual, such methods may be considered as arranging them as parallel inscriptions or printed matter on the transformation result (graph) display.
However, with such a visualization mode, it is extremely difficult to capture the continuous fluctuation of characteristic peak time series of the wavelet transformation results. Also, when continuously comparing the time series variation, a very wide space is required for the consecutive arrangement of the results.
Moreover, when the period of the pilot signal is not known in advance, it is not possible appropriately to divide the target waveform for time-frequency analysis by wavelet transformation, and thus it is difficult to study the behavior of the characteristic peak time series (that is to say, time sequence) in the wavelet transformation results.
Accordingly, one objective of the present invention is to provide a novel waveform signal analyzer that may detect the periodicity of an input waveform signal and divide the waveform signal by the relevant period, and may readily compare the time series-wise fluctuations of the wavelet transformation results for the divided waveform signal.
A further objective of the present invention is to provide a waveform signal analyzer that may detect the periodicity of a waveform signal from wavelet transformation results, and may readily compare the time series-wise fluctuations of the wavelet transformation results by dividing the wavelet transformation results by the relevant period.
In order to achieve the above objectives, the present invention is a waveform signal analyzer that comprises:
an input means for inputting a waveform signal;
a period detection means for detecting the period of the waveform signal inputted by the input means;
a division means for dividing the waveform signal based on the period detected by the period detection means;
a wavelet transformation means for the wavelet-transforming each individual division of the waveform signal; and
a display means for consecutively displaying in time series the wavelet transformation results of each individual division of the waveform signal.
When using the present invention, the period of the waveform signal is detected by the period detection means, and the waveform signal is divided by that period. Therefore, study of the time series behavior of the characteristic peaks in the wavelet transformation results is simple. Also, when using the present invention, because the wavelet transformation results of each individual division of the waveform signal are consecutively displayed in time series, comparison and examination of wavelet transformation results may be simply performed.
Furthermore, the present invention is a waveform signal analyzer that comprises:
an input means for inputting a waveform signal;
a wavelet transformation means for performing wavelet transformation of the waveform signal inputted by the input means;
a period detection means for detecting the period of transformation results from the wavelet transformation means;
a division means for dividing the transformation results on the basis of the period detected by the period detection means; and
a display means for consecutively displaying the divided transformation results in time series.
When using the present invention, because the period of the wavelet transformation results is detected by the period detection means and the wavelet transformation results are divided by that relevant period, study of the time series-wise behavior of characteristic peaks in the wavelet transformation results is simple. Also, when using the present invention, because the divided wavelet transformation results are consecutively displayed in time series, comparison and examination of each divided wavelet transformation result may be simply performed.